دورية أكاديمية
Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning.
| العنوان: | Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning. |
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| المؤلفون: | Mao W; State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China., Wei B; State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China., Xu X; State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China., Chen L; State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China., Wu T; State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China., Peng Z; State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China., Ren C; State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China. |
| المصدر: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Jun 13; Vol. 22 (12). Date of Electronic Publication: 2022 Jun 13. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Original Publication: Basel, Switzerland : MDPI, c2000- |
| مستخلص: | The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnosis method based on deep neural networks and a semi-supervised transfer learning framework called adaptive reinforcement (AR) to solve the above limitations. The innovation of this framework consists of its enhancement of the consistency regularization algorithm. The experiments were conducted on real-world 110 kV power transformers' three-phase fault grounding currents of the iron cores from various devices with four types of faults: Phases A, B, C and ABC to ground. We trained the model on the source domain and then transferred the model to the target domain, which included the unbalanced and undefined fault datasets. The results show that our proposed model reaches over 95% accuracy in classifying the type of fault and outperforms other popular networks. Our AR framework fits target devices' fault data with fewer dozen epochs than other novel semi-supervised techniques. Combining the deep neural network and the AR framework helps diagnose the power transformers, which lack diagnosis knowledge, with much less training time and reliable accuracy. |
| References: | Sensors (Basel). 2019 Apr 17;19(8):. (PMID: 30999589) Sensors (Basel). 2022 May 21;22(10):. (PMID: 35632320) |
| معلومات مُعتمدة: | 520940200067 China's State Grid Shanghai Electric Power Company |
| فهرسة مساهمة: | Keywords: deep neural network; fault type diagnosis of power transformers; semi-supervised transfer learning; three-phase grounding current of the iron core |
| تواريخ الأحداث: | Date Created: 20220624 Latest Revision: 20220716 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC9231397 |
| DOI: | 10.3390/s22124470 |
| PMID: | 35746252 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1424-8220 |
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| DOI: | 10.3390/s22124470 |